Topics of expertise  include modeling and analysis of complex networks, both social and physical, design of large-scale recommender systems, analysis of heterogeneous and complex EHR and other health data, learning from distributed sensor data acquired from intelligent transportation systems, multi-modal machine learning, etc. Seminal advances have been made in the theoretical (sample complexity, statistical bounds, etc), algorithmic, statistical and hardware/software implementational  aspects associated with such  problems. We have also deployed several data-driven solutions that  interface with real cyber-physical systems. 

Four papers from UT-MINDS authors at AISTATS 2018! (Numbers 60, 121, 272 & 637, [[https://www.aistats.org/aistats2018/accepted.html | AISTATS 2018 Accepted Papers]]) Congratulations to Rahi Kalantari, [[https://rjvak7.github.io/ | Rajiv Khanna]] and [[https://rajatsen91.github.io/ | Rajat Sen]]!

Four papers from UT-MINDS authors at AISTATS 2018! (Numbers 60, 121, 272 & 637, [[https://www.aistats.org/aistats2018/accepted.html | AISTATS 2018 Accepted Papers]]) Congratulations to Rahi Kalantari, [[https://rjvak7.github.io Rajiv Khanna]] and [[https://rajatsen91.github.io/ | Rajat Sen]]!

Four papers from UT-MINDS authors at AISTATS 2018! (Numbers 60, 121, 272 & 637, [[https://www.aistats.org/aistats2018/accepted.html | AISTATS 2018 Accepted Papers]]) Congratulations to Rahi Kalantari, [[https://rjvak7.github.io Rajiv Khanna] and [[https://rajatsen91.github.io/ | Rajat Sen]]!

Four papers from UT-MINDS authors at AISTATS 2018! (Numbers 60, 121, 272 & 637, [[https://www.aistats.org/aistats2018/accepted.html | AISTATS 2018 Accepted Papers]]) Congratulations to Rahi Kalantari and [[https://rajatsen91.github.io/ | Rajat Sen]]!

Two papers from UT-MINDS authors at AISTATS 2018! (Numbers 272 & 637, [[https://www.aistats.org/aistats2018/accepted.html | AISTATS 2018 Accepted Papers]]) Congratulations to Rahi Kalantari and [[https://rajatsen91.github.io/ | Rajat Sen]]!

[[https://icml.cc/Conferences/2018/AcceptedPapersInitial | Two papers at ICML 2018, a top ML conference,]] from UT-MINDS authors!

Two papers from UT-MINDS authors at AISTATS 2018! (Numbers 272 & 637, [[https://www.aistats.org/aistats2018/accepted.html | AISTATS 2018 Accepted Papers]]) Congratulations to Rahi Kalantari and [[https://rajatsen91.github.io/ | Rajat Sen]]!

[[https://icml.cc/Conferences/2018/AcceptedPapersInitial | Two papers at ICML]] from UT-MINDS authors!

!!![[https://http://wncg.org/affiliates | UT-MINDS invites industry partners through its AFFILIATES program]].

[[https://http://wncg.org/affiliates | UT-MINDS invites industry partners through its AFFILIATES program]].

[[http://www.aistats.org/| Profs. Ghosh, Dimakis and ShakKottai and their students to present an unprecedented 6 papers at AISTATS, April 2017.]] AISTATS is among the top venues for research on machine learning.

[[http://www.aistats.org/| Profs. Ghosh, Dimakis and Shakottai and their students to present an unprecedented 6 papers at AISTATS, April 2017.]] AISTATS is among the top venues for research on machine learning.

[[https://nips.cc/Conferences/2017/AcceptedPapersInitial | Three papers from UT-MINDS authors at NIPS 2017. Congrats!]] This is a top international conference  on machine learning.

[[Machine Learning for Healthcare startup Accordion Health acquired]] by publicly listed health analytics company, Evolent Heath.  All 3 founders of Accordion Health are from UT-MINDS.

[[Machine Learning for Healthcare startup Accordion Health acquired by publicly listed health analytics company, Evolent Heath.]].  All 3 founders of Accordion Health are from UT-MINDS.

[[Machine Learning for Healthcare startup Accordion Health acquired by publicly listed health analytics company, Evolent Heath]] All the founders of Accordion Health came from UT-MINDS.

!!!!! The [[http://wncg.org | WNCG]] initiative in Machine INtelligence and Decision Systems (UT-MINDS) involves research in large-scale data mining and machine learning, with applications to solving complex engineering problems.  Topics include modeling and analysis of complex networks, both social and physical, design of large-scale recommender systems, analysis of heterogeneous and complex EHR and other health data, learning from distributed sensor data acquired from intelligent transportation systems,  etc. Seminal advances have been made in the theoretical (sample complexity, statistical bounds, etc), algorithmic, statistical and implementational  aspects associated with such  problems. We have also deployed several data-driven solutions that  interface with real cyber-physical systems. 

[[https://2017.icml.cc/| Prof. Alex Dimakis has 6 papers accepted at ICML, 2017. Congrats!]] This is a top international conference  on machine learning.

[[https://nips.cc/Conferences/2017/AcceptedPapersInitial | Three papers from UT-MINDS authors at NIPS 2017. Congrats!]] This is a top international conference  on machine learning.

>>hero-unit<<

!! Data Science & Engineering @ [[http://wncg.org | WNCG]]

!!!!! The [[http://wncg.org | WNCG]] initiative in Data Science and Engineering involves research in large-scale data mining and machine learning, with applications to solving complex engineering problems.  Topics include modeling and analysis of complex networks, both social and physical, design of large-scale recommender systems, analysis of heterogeneous and complex EHR and other health data, learning from distributed sensor data acquired from intelligent transportation systems,  etc. Seminal advances have been made in the theoretical (sample complexity, statistical bounds, etc), algorithmic, statistical and implementational  aspects associated with such  problems. We have also deployed several data-driven solutions that  interface with real cyber-physical systems. 

[[https://www.cognitivescale.com/press-release/cognitivescale-welcomes-dr-joydeep-ghosh-chief-scientific-officer-world-renowned-artificial-intelligence-authority-joins-leadership-team/| Prof. Ghosh appointed as Chief Scientific Officer of CognitiveScale]] CognitiveScale incorporates several AI technologies including advanced machine learning for industry-scale smart solutions, and counts several Fortune 500 Companies among its clients. Prof. Ghosh will continue to be a faculty at UT;  however this engagement showcases the relevance of our group to new and innovative AI companies.

[[https://www.cs.umn.edu/activity/talks/cray|Prof. Ghosh gives Plenary Talks at TCS and at Univ. of Minnesota's Cray Distinguished Lecture series, on Extracting Meaningful Insights from Complex Medical Data]]

[[|Team headed by Prof. Ghosh receives 2017 Distinguished Clinical Informatics Research Paper Award at AMIA for PheKnow-Cloud: A Tool for Evaluating High-Throughput Phenotype Candidates using Online Medical Literature ]]

[[Team headed by Prof. Ghosh receives 2017 Distinguished Clinical Informatics Research Paper Award at AMIA for PheKnow-Cloud: A Tool for Evaluating High-Throughput Phenotype Candidates using Online Medical Literature ]]

[[Team headed by Prof. Ghosh receives 2017 Distinguished Clinical Informatics Research Paper Award at AMIA for "PheKnow-Cloud: A Tool for Evaluating High-Throughput Phenotype Candidates using Online Medical Literature" ]] >><<

[[https://youtu.be/ebjtkdocPJk | Prof. Ghosh receives 2015 IEEE CS Technical Achievement Award for his work on multi-learner systems]]

[[https://wncg.org/news/facultypositions2015 | Recent Students Accept Faculty Positions at Illinois-Urbana, Cornell and Emory]]

Joyce Ho and Yubin Park are '''2014 Code-a-Palooza Winners''' at [[http://healthdatapalooza.org/news/ \ Datapalooza 2014]]

Alumna Suju Rajan receives the '''Best Paper Award at RecSys14''' for "Beyond Clicks: Dwell Time for Personalization"

Joyce Ho and Yubin Park are '''2014 Code-a-Palooza Winners''' at Datapalooza 2014

!!!!! The [[http://wncg.org | WNCG]] initiative in Data Science and Engineering involves research in large-scale data mining and machine learning, with applications to solving complex engineering problems.  Topics include modeling and analysis of complex networks, both social and physical, analysis of heterogeneous and complex EHR and other health data, learning from distributed sensor data acquired from intelligent transportation systems, etc. Seminal advances have been made in the theoretical (sample complexity, statistical bounds, etc), algorithmic, statistical and implementational  aspects associated with such  problems. We have also deployed solutions that  interface with real cyber-physical systems. 

!!!!! The [[http://wncg.org | WNCG]] initiative in Data Science and Engineering involves research in large-scale data mining and machine learning, and their application to solving complex engineering problems that involve the modeling and analysis of complex networks, both social and physical, heterogeneous and complex EHR and other health data, distributed sensor data from intelligent transportation systems, etc. Seminal advances have been made in the theoretical (sample complexity, statistical bounds, etc), algorithmic, statistical and implementational  aspects associated with such  problems. We have also deployed solutions that  interface with real cyber-physical systems as well as the ambient world of data, signals and control.

!! Data Science & Engineering @ WNCG

[[https://wncg.org/news/wncg-faculty-and-students-showcase-innovation-during-sxsw-2015 | Making Big Data Digestible Panel at SXSW 2015]]

[[https://wncg.org/news/wncg-faculty-and-students-showcase-innovation-during-sxsw-2015 |
Making Big Data Digestible Panel at SXSW 2015]]

'Making Big Data Digestible' Panel at SXSW 2015]]

"Making Big Data Digestible" Panel at SXSW 2015]]

!!!!! The WNCG initiative in Data Science and Engineering involves research in large-scale data mining and machine learning, and their application to solving complex engineering problems that involve the modeling and analysis of complex networks, both social and physical, heterogeneous and complex EHR and other health data, distributed sensor data from intelligent transportation systems, etc. Seminal advances have been made in the theoretical (sample complexity, statistical bounds, etc), algorithmic, statistical and implementational  aspects associated with such  problems. We have also deployed solutions that  interface with real cyber-physical systems as well as the ambient world of data, signals and control.

The Data Science & Engineering initiative currently involves over 30 graduate students within WNCG. The listed faculty members also supervise several students from other areas of ECE, as well as from other departments at UT such as Computer Sciences, Statistical and Data Sciences and Biomedical Engineering.

Recent graduates are working at companies such as Google, Yahoo, Qualcomm, Facebook and Microsoft Research, or serving in faculty positions at top universities such as Illinois-Urbana (CS), Cornell (OR), Emory (CS), Minnesota (CS)  ADD MORE.

Edison Series Brings Big Data to Middle-School and High-School Students
http://wncg.org/news/edison-series-brings-big-data-middle-school-and-high-school-students

Digesting Big Data Panel at SXSW 2015
https://wncg.org/news/wncg-faculty-and-students-showcase-innovation-during-sxsw-2015

!! Data Science and Engineering

!!!!! The WNCG initiative in Data Science and Engineering involves research in large-scale data mining and machine learning, and their application to solving complex engineering problems that involve the modeling and analysis of complex networks, both social and physical, heterogeneous and complex EHR and other health data, distributed sensor data from intelligent transportation systems, etc.Seminal advances have been made in the theoretical (sample complexity, statistical bounds, etc), algorithmic, statistical and implementational (Hadoop/Spark; visualization) aspects of these problems, in addition to creating working interfaces with real cyber-physical systems and the ambient world of data, signals and control.

This thrust currently involves over 30 graduate students within WNCG. The faculty members listed above also supervise several students from other areas of ECE, as well as from other departments at UT such as Computer Sciences, Statistical and Data Sciences and Biomedical Engineering.

Recently alumni are working at companies such as Google, Yahoo, Qaualcomm, Facebook and Microsoft Research, and or have found faculty positions in top universities such as Illinois-Urbana (CS), Cornell (OR), Emory (CS), Minnesota (CS)  ADD MORE.

!!!! The WNCG initiative in Data Science and Engineering involves research in large-scale data mining and machine learning, and their application to solving complex engineering problems that involve the modeling and analysis of complex networks, both social and physical, heterogeneous and complex EHR and other health data, distributed sensor data from intelligent transportation systems, etc.Seminal advances have been made in the theoretical (sample complexity, statistical bounds, etc), algorithmic, statistical and implementational (Hadoop/Spark; visualization) aspects of these problems, in addition to creating working interfaces with real cyber-physical systems and the ambient world of data, signals and control.

Recently alumni are working at companies such as Google, Yahoo, Qaualcomm, Facebook and Microsoft Research, and or have found faculty positions in top universities such as Illinois-Urbana (CS), Cornell (OR), Emory (CS), Minnesota (CS)  ADD MORE.

!!! The WNCG initiative in Data Science and Engineering involves research in large-scale data mining and machine learning, and their application to solving complex engineering problems that involve the modeling and analysis of complex networks, both social and physical, heterogeneous and complex EHR and other health data, distributed sensor data from intelligent transportation systems, etc.Seminal advances have been made in the theoretical (sample complexity, statistical bounds, etc), algorithmic, statistical and implementational (Hadoop/Spark; visualization) aspects of these problems, in addition to creating working interfaces with real cyber-physical systems and the ambient world of data, signals and control.

!!!! This thrust currently involves over 30 graduate students within WNCG. The faculty members listed above also supervise several students from other areas of ECE, as well as from other departments at UT such as Computer Sciences, Statistical and Data Sciences and Biomedical Engineering.

!!!! Recently alumni are working at companies such as Google, Yahoo, Qaualcomm, Facebook and Microsoft Research, and or have found faculty positions in top universities such as Illinois-Urbana (CS), Cornell (OR), Emory (CS), Minnesota (CS)  ADD MORE.

! The WNCG initiative in Data Science and Engineering involves research in large-scale data mining and machine learning, and their application to solving complex engineering problems that involve the modeling and analysis of complex networks, both social and physical, heterogeneous and complex EHR and other health data, distributed sensor data from intelligent transportation systems, etc.Seminal advances have been made in the theoretical (sample complexity, statistical bounds, etc), algorithmic, statistical and implementational (Hadoop/Spark; visualization) aspects of these problems, in addition to creating working interfaces with real cyber-physical systems and the ambient world of data, signals and control.

!! This thrust currently involves over 30 graduate students within WNCG. The faculty members listed above also supervise several students from other areas of ECE, as well as from other departments at UT such as Computer Sciences, Statistical and Data Sciences and Biomedical Engineering.

!! Recently alumni are working at companies such as Google, Yahoo, Qaualcomm, Facebook and Microsoft Research, and or have found faculty positions in top universities such as Illinois-Urbana (CS), Cornell (OR), Emory (CS), Minnesota (CS)  ADD MORE.

! Welcome!

< some short blurb >

[Link to] Faculty
    < will contain a list of relev faculty and their photos >

[Link to] Projects
    < will contain short descriptions, with pictures, of projects from each of us >

[Link to] Classes
    <  will contain a list of relevant classes, possibly with descriptions >

[Link to] Students, Postdocs and Alumni
    < current and updated lists of students and alums >

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